Quantitative methods
As the name suggests, these are methods that require accumulating and analysing data in numerical format. The most frequently used quantitative methods are questionnaires and surveys. This involves asking a targeted group of people for their opinion on a subject. This could be to evaluate something (an exhibit in a museum, an event, etc.), or an attitudinal survey. It requires careful thought on the construction and delivery of the questionnaire, identification and location of participants. It’s highly unlikely that you will have enough participants for the data to be statistically significant. Also, given that questions like how? and why? are more interesting than how many?, I recommend that you use questionnaires sparingly and concentrate on qualitative methods instead.
Writing up quantitative results
If you have used a questionnaire, for example, you would give the results in a suitable format (charts, graphs or tables). Don’t report results in percentages for small sample sizes. Your results won’t be scalable and this gives a veneer of significance that isn’t really justifiable.
Qualitative methods
These methods don’t involve number crunching but they do pose challenges for writing up data. I won’t give an exhaustive account of methodologies here as you can look up what these involve, but these are some you could consider:
Interviews
You may want to interview individuals who are stake-holders in your subject of investigation in order to elicit opinions or information. You will therefore need to think carefully about the questions you want to ask in order to elicit the best responses. The way in which you ask a question has an effect on the answer (e.g. you’re like to get very different responses to these two questions: ‘Do you favour the killing of unborn babies in order to extract their stem cells for medical research?’ and ‘Do you favour the use of existing stem cells in research to cure or treat dozens of serious diseases like Alzheimer’s and diabetes?’). Make sure your questions are as neutral as possible. You will need good interview techniques. Ask permission to record the interviews, and it is also best to take notes even if you are recording the interview. Here is a useful tip sheet.
Focus groups
This involves bringing a group of people (usually between 4 and 8) together in which you facilitate discussion by asking stimulus questions and analysing the group discussion. As a facilitator you should not participate in the discussion: your role is to provide the impetus and then sit back and be a neutral observer. Again, recording the discussion will be invaluable for analysing it. This website is a good starting point.
Case study
This is where you choose a particular ‘case’ and analyse it from multiple perspectives in order to gain an in-depth understanding of it. You will probably use a variety of methods to gather data (interviews, observations, document collection). A good case study investigates a ‘problem’ and comes up with some recommendations. This article provides a useful introduction, but bear in mind that it is aimed at PhD students and you should scale things right down.
Ethnography
This is the study of interactions within social groups or communities in order to understand how culture and ideologies shape behaviours, perceptions and actions. This can get hugely complicated and time-consuming, but small-scale observational studies can be useful in coming up with a problem for a case study and also to provide some context for analyzing data gathered by other means. A good introduction is provided by this article.
Writing up qualitative results
Writing up qualitative data can be tricky because it isn’t always immediately obvious what there is to analyse. You should aim to pull out certain themes or strands of argument that emerge from you data. You could, if you wish, draw up a coding frame which is merely a list of criteria against which you assess your data (e.g. you could code across a number of interviews how many people feel strongly negatively and positively about a particular issue). The data you use in support of an argument should be directly linked to the research question. Present your data clearly in the text, so that quotations and their origins are clearly identifiable, e.g. if you are quoting directly or if you are analyzing interpretations of what the interviewed persons said. The citations or other illustrations must be clearly contextualised. If it is observational material, state whether you collected the data yourself or if you used data collected by someone else. Don’t get bogged down in giving too much raw data – there needs to be judicious but unbiased selection of relevant findings.
In addition to collecting your own data, you need to show that you have read about your topic and consulted a variety of sources. You can be imaginative when it comes to sources; they don’t all need to be academic papers or books. Images are a useful source of information, and you can also use audiovisual sources. It is essential to acknowledge your sources by citing them in the text (Harvard style is preferred) and in the reference list. Remember to cite the actual work you consulted (e.g. if you find old texts on the internet, you need to say so, e.g. (Darwin, 1861, cited in evolution.com, 2013). Interpolate your sources into your discussion, where appropriate, rather than merely providing a citation at the end of the sentence [e.g. ‘A survey by Donheim (1998) of 106 people, found that only 58 were in favour of the proposition’, rather than ‘Fifty-eight people were in favour of the proposition (Donheim, 1998)’]. If you have used the words of others verbatim, these must appear in quotation marks, cited, and listed in the bibliography. Mendeley is an excellent way of keeping track of your references and your own data.
You are encouraged to use images in your report, but these must be captioned properly and you must state the source of the image (put your own name if you are the photographer).